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Item selection by latent class-based methods: an application to nursing home evaluation

Author

Listed:
  • Francesco Bartolucci

    (University of Perugia)

  • Giorgio E. Montanari

    (University of Perugia)

  • Silvia Pandolfi

    (University of Perugia)

Abstract

The evaluation of nursing homes is usually based on the administration of questionnaires made of a large number of polytomous items to their patients. In such a context, the latent class model represents a useful tool for clustering subjects in homogenous groups corresponding to different degrees of impairment of the health conditions. It is known that the performance of model-based clustering and the accuracy of the choice of the number of latent classes may be affected by the presence of irrelevant or noise variables. In this paper, we show the application of an item selection algorithm to a dataset collected within a project, named ULISSE, on the quality-of-life of elderly patients hosted in Italian nursing homes. This algorithm, which is closely related to that proposed by Dean and Raftery in 2010, is aimed at finding the subset of items which provides the best clustering according to the Bayesian Information Criterion. At the same time, it allows us to select the optimal number of latent classes. Given the complexity of the ULISSE study, we perform a validation of the results by means of a sensitivity analysis, with respect to different specifications of the initial subset of items, and of a resampling procedure.

Suggested Citation

  • Francesco Bartolucci & Giorgio E. Montanari & Silvia Pandolfi, 2016. "Item selection by latent class-based methods: an application to nursing home evaluation," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 245-262, June.
    • Handle: RePEc:spr:advdac:v:10:y:2016:i:2:d:10.1007_s11634-016-0232-3
    DOI: 10.1007/s11634-016-0232-3
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    References listed on IDEAS

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    1. Nema Dean & Adrian Raftery, 2010. "Latent class analysis variable selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 11-35, February.
    2. Ofer Harel & Joseph L. Schafer, 2009. "Partial and latent ignorability in missing-data problems," Biometrika, Biometrika Trust, vol. 96(1), pages 37-50.
    3. Galasso, Vincenzo & Profeta, Paola, 2007. "How does ageing affect the welfare state?," European Journal of Political Economy, Elsevier, vol. 23(2), pages 554-563, June.
    4. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    5. Friedrich Breyer & Joan Costa-Font & Stefan Felder, 2010. "Ageing, health, and health care," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 26(4), pages 674-690, Winter.
    6. Karlis, Dimitris & Xekalaki, Evdokia, 2003. "Choosing initial values for the EM algorithm for finite mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 577-590, January.
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    1. Francesco Bartolucci & Giorgio E. Montanari & Silvia Pandolfi, 2018. "Latent Ignorability and Item Selection for Nursing Home Case-Mix Evaluation," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 172-193, April.

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